Personalized ranking: A contextual ranking approach

Gae Won You, Seung Won Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, in the context of text retrieval, in contrast to traditional web search engines retrieving the same results for all users, major commercial search engines are starting to support personalization, improving the search quality by adapting to the user-specific retrieval contexts, e.g., prior search history or other application contexts. This paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt context-sensitive ranking model to formalize personalization as a cost-based optimization over context-sensitive rankings collected. With this formalism, personalization is essentially retrieving the context-sensitive ranking matching the specific user's retrieval context and generating a personalized ranking accordingly. In particular, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over real-life data validate both the effectiveness and efficiency of our framework.

Original languageEnglish
Title of host publicationProceedings of the 2007 ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Print)1595934804, 9781595934802
Publication statusPublished - 2007
Event2007 ACM Symposium on Applied Computing - Seoul, Korea, Republic of
Duration: 2007 Mar 112007 Mar 15

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Other2007 ACM Symposium on Applied Computing
CountryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Software

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